CN117875889A - Digital management method and system for field operation - Google Patents
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Abstract
The application provides a digital management method and a digital management system for field operation, which relate to the technical field of digital management, wherein the method comprises the following steps: the method comprises the steps of automatically establishing operation activities through task demands, generating operation activity demands to be distributed, then establishing an operator file, inputting the operator file and the operation activity demands to be distributed into a self-adaptive evaluation network, executing task matching degree evaluation with task time limit as limit constraint, acquiring matching degree evaluation results, and then carrying out association binding between the operation activities and operators to complete digital management. The method mainly solves the problems that the traditional recording method depends on manual operation, so that the efficiency is low, errors are easy to occur, the method also lacks flexibility, and the requirements of different roles on data analysis cannot be met rapidly. The accuracy and timeliness of the data are improved, the problem that the internal management of the base layer lacks digital support can be effectively solved, and the management efficiency and decision making capability are improved.
Description
Technical Field
The application relates to the technical field of digital management, in particular to a digital management method and system for field operation.
Background
The enterprise information systems of most industries mainly provide support for upper layer management decisions, but in the basic level units, due to obvious professional differences and frequent management demands, the traditional system development mode is often high in cost and short in life cycle, and a large amount of resources are wasted. Therefore, how to meet the requirements of the basic level of enterprises, and adapt to changeable management conditions, so that basic level units really realize informatization and digital management, and comprehensively improve the management efficiency of the enterprises, and the basic level units become the main advantages of products. The 'on site' is a digital management platform which is focused on the field operation of the basic level of enterprises. The method is used for meeting the requirements of various business scenes of operators, management staff and decision makers, and ensuring comprehensive recording of the operation activities, traceability of the operation process and timely grasping of the operation dynamics. Through the platform, standardized operation of enterprises is comprehensively promoted, so that production efficiency is improved, and efficient management of the enterprises on potential safety hazards is effectively supported.
In the process of realizing the technical scheme of the invention in the embodiment of the application, the technology at least has the following technical problems:
the traditional recording method depends on manual operation, so that the efficiency is low, errors are easy to occur, and the method also lacks flexibility and cannot rapidly meet the requirements of different roles on data analysis.
Disclosure of Invention
The method mainly solves the problems that the traditional recording method depends on manual operation, so that the efficiency is low, errors are easy to occur, the method also lacks flexibility, and the requirements of different roles on data analysis cannot be met rapidly.
In view of the foregoing, the present application provides a digital field operation management method and system, and in a first aspect, the present application provides a digital field operation management method, where the method includes: acquiring a job activity, wherein the job activity stores a job type, job content, a job object, task time limit, post skills, a metering mode and frequency, and the job activity is automatically created through task requirements; establishing an operator file, wherein the operator file is a file constructed by historical operation data of interactive operators, and comprises an operator skill level file, an operator trust file and a real-time task file; inputting the worker files and the job requirements to be distributed into an adaptive evaluation network, and executing task matching degree evaluation taking task time limit as limit constraint; obtaining a matching degree evaluation result, wherein the matching degree evaluation result is an output result of the self-adaptive evaluation network; and completing the association binding with the operator through the matching degree evaluation result, distributing a work unit package, and performing operation digital management.
In a second aspect, the present application provides a field operation digital management system, the system comprising: the automatic creation module is used for acquiring a job activity, wherein the job activity stores a job type, job content, a job object, task time limit, post skills, a metering mode and frequency, and the job activity is automatically created through task requirements; the job demand generation module is used for synchronizing the job activities to a job activity space and generating job demands to be distributed; the system comprises an operator file establishing module, a real-time task file and a user file establishing module, wherein the operator file establishing module is used for establishing an operator file, the operator file is a file constructed by historical operation data of interactive operators, and the operator file comprises an operator skill level file, an operator trust file and a real-time task file; the matching degree evaluation execution module is used for inputting the operator files and the job requirements to be distributed into the self-adaptive evaluation network and executing task matching degree evaluation taking task time limit as limit constraint; the evaluation result acquisition module is used for acquiring a matching degree evaluation result, wherein the matching degree evaluation result is an output result of the self-adaptive evaluation network; and the digital management module is used for completing the association binding with the operators through the matching degree evaluation result, distributing the working unit package and carrying out the digital management of the operations.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the application provides a digital management method and a digital management system for field operation, which relate to the technical field of digital management, wherein the method comprises the following steps: the method comprises the steps of automatically establishing operation activities through task demands, generating operation activity demands to be distributed, then establishing an operator file, inputting the operator file and the operation activity demands to be distributed into a self-adaptive evaluation network, executing task matching degree evaluation with task time limit as limit constraint, acquiring matching degree evaluation results, and then carrying out association binding between the operation activities and operators to complete digital management.
The method mainly solves the problems that the traditional recording method depends on manual operation, so that the efficiency is low, errors are easy to occur, the method also lacks flexibility, and the requirements of different roles on data analysis cannot be met rapidly. The accuracy and timeliness of the data are improved, the problem that the internal management of the base layer lacks digital support can be effectively solved, and the management efficiency and decision making capability are improved.
The foregoing description is merely an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
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For a clearer description of the technical solutions of the present application or of the prior art, the drawings used in the description of the embodiments or of the prior art will be briefly described below, it being obvious that the drawings in the description below are only exemplary and that other drawings can be obtained, without inventive effort, by a person skilled in the art from the drawings provided.
Fig. 1 is a schematic flow chart of a digital management method for field operation according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a method for outputting a matching degree evaluation result in a digital management method for field operation according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of a method for performing digital management of an operation in a digital management method for on-site operation according to an embodiment of the present application;
FIG. 4 is a schematic structural diagram of a digital management system for field operations according to an embodiment of the present application;
FIG. 5 is a schematic flow chart of creating job types according to an embodiment of the present application;
fig. 6 is a schematic diagram of a relationship between unit work packages according to an embodiment of the present application.
Reference numerals illustrate: the system comprises an automation creation module 10, a job demand generation module 20, a job personnel file establishment module 30, a matching degree evaluation execution module 40, an evaluation result acquisition module 50 and a digital management module 60.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The method mainly solves the problems that the traditional recording method depends on manual operation, so that the efficiency is low, errors are easy to occur, the method also lacks flexibility, and the requirements of different roles on data analysis cannot be met rapidly. The accuracy and timeliness of the data are improved, the problem that the internal management of the base layer lacks digital support can be effectively solved, and the management efficiency and decision making capability are improved.
For a better understanding of the foregoing technical solutions, the following detailed description will be given with reference to the accompanying drawings and specific embodiments of the present invention:
example 1
A method for digitally managing field operations as shown in fig. 1, the method comprising:
acquiring a job activity, wherein the job activity stores a job type, job content, a job object, task time limit, post skills, a metering mode and frequency, and the job activity is automatically created through task requirements;
Specifically, the job types are created before the job activity is acquired, and as shown in fig. 5, the job types are created including: setting a job object type, setting a job execution frequency, setting a job content, setting a job post skill, setting a job metering mode, setting a job standard reference, then issuing a job type, and compiling a work unit package, wherein as shown in fig. 6, the work unit package and a attribution unit are n:1, and the network exhibits 1:1, and the job object (key point) is n: n, and the job type is n:1, wherein the network comprises a working point position, a working object can obtain the working point position, a working type comprises post skills, metering modes and frequencies, a working content item and a working object type, and the working content item are 1: n, the job object type and job object are 1: n, acquiring the job activity to store task content, task time limit and skill requirements, and automatically creating through the task requirements. The job activities include: job type, job content, job object, task time limit, post skill, metering mode, frequency. Advanced data collection: information about the task is collected from a project management system, task distribution system, or other source, including task content, task time limits, and skills required. The task content comprises: whether the valve is normal, whether the air tightness is normal, whether the vertical pipe is normal, and the like, and the task time limit comprises: how often or how often one work is, the required skills include: for example, the security check operation is to require the operators to have the related skills and the related post certificate requirements aiming at the security check operation of residential users. And (3) data processing: processing, e.g., sorting, classifying, and normalizing, the collected data to create a job campaign: the processed data is stored in new job activities, each of which contains task content, task time limits, and skill requirements. Automated creation task: and automatically creating corresponding tasks according to the information in the job activities. This may include creating new task entries in the project management system or task allocation system, or sending information to the relevant personnel to manually create the task. Through the steps, an effective job activity can be established to store task content, task time limit and skill requirements, and corresponding tasks can be automatically established through task requirements. This helps to improve job efficiency, reduce the error rate of task allocation, and ensure that items are completed on time.
Synchronizing the operation activities to an operation activity space to generate operation activity demands to be distributed;
specifically, the job activity is synchronized to a job activity space and job activity requirements to be allocated are generated, and the job activity space is defined: the job activity space is a space for storing and managing job activities, and may be a database, a cloud platform, or a specific management system. Synchronizing job activities: the created job activity is synchronized into the job activity space. This may be by importing information of the job activity into the job activity space, or transferring data from the job activity storage location to the job activity space via an API or other data transfer means. Generating job activity requirements to be allocated: in the job activity space, a demand for job activities to be allocated is generated according to task time limits, skill demands, and other factors. This may include identifying tasks that are about to expire, job activities that lack a particular skill, or determining tasks that need to be assigned based on project requirements and other criteria. The job activities include: whether the valve state is normal, whether the valve well lid is good, whether all parts of the valve inside the valve well are good, whether leakage exists, whether water accumulation exists, whether other abnormal conditions exist, and the like. Assigning job activities to be assigned: the job activities to be distributed are distributed to the appropriate personnel or team according to the demands of the job activities to be distributed. This may be done manually, automatically, or by bidding etc. Through the steps, the operation activities can be synchronized to the operation activity space, and the operation activity requirement to be allocated is generated, so that the tasks are ensured to be allocated timely and accurately, and the project execution efficiency and the resource utilization efficiency are improved.
Establishing an operator file, wherein the operator file is a file constructed by historical operation data of interactive operators, and comprises an operator skill level file, an operator trust file and a real-time task file;
specifically, an operator profile is established, and based on historical operation data of the interactive operator, the operator skill level profile is established: this section of the profile records the skill level of the worker. It may be an assessment of the expertise and skills of an operator in a particular area or task. For example, skill levels, knowledge reserves, the ability to solve problems, etc., exhibited by the operator in the task performed in the past may be incorporated into this profile. Worker trust profile: this profile records the trust of the worker in the collaboration process. It may be based on the performance of the worker in past interactions and collaboration, such as a comprehensive assessment of factors such as the rate of completion of tasks on time, the quality of the job, the effectiveness of communication, etc. The trust level of the worker may dynamically change with time and with increasing number of cooperatives. Real-time task archives: the part of the archive records the current task state and the historical task completion condition of the operator. It may include real-time information such as task lists, task progress, task evaluations, etc. that the operator is currently performing or has completed. Therefore, a manager can conveniently monitor and adjust the working state of the operator in real time. By establishing such an operator profile, the enterprise can better understand the operator's ability and performance, thereby more effectively performing task allocation and resource allocation. Meanwhile, staff can know the skill level, the trust level and the task state of the staff by looking up the files of the staff, so that the staff can plan the work of the staff better.
Inputting the worker files and the job activity demands to be distributed into an adaptive evaluation network, and executing task matching degree evaluation taking task time limit as limit constraint;
specifically, an operator profile and job activity requirements to be assigned are entered: and taking the worker files and the job activity demands to be distributed as input data, and inputting the input data into the self-adaptive evaluation network. Constructing a task matching degree evaluation model: and constructing a task matching degree evaluation model in the self-adaptive evaluation network. The model takes task time limit as limit constraint, comprehensively considers factors such as skill level of operators, trust level of the operators, real-time task files and the like, and evaluates the matching degree of each to-be-allocated operation activity. Calculating the task matching degree: and calculating the matching degree of the job activities to be allocated according to the constructed task matching degree evaluation model. The degree of matching may be a comprehensive evaluation value indicating the suitability of the worker to complete the task. Ranking and recommending: and sequencing the operation activities to be distributed according to the calculated task matching degree, and recommending sequencing results to corresponding operators. Updating the worker file: after the task is completed, the skill level file, the trust level file and the real-time task file of the operator are updated according to the task completion condition and feedback. Through the steps, the operator files and the demands of the work activities to be distributed can be input into the adaptive evaluation network, and the task matching degree evaluation which takes the task time limit as the limit constraint is executed, so that a more accurate and more efficient solution is provided for task distribution.
Obtaining a matching degree evaluation result, wherein the matching degree evaluation result is an output result of the self-adaptive evaluation network;
specifically, the output result of the adaptive evaluation network is read: and after the self-adaptive evaluation network finishes the evaluation of the input operator files and the demands of the work activities to be distributed, reading the output result of the self-adaptive evaluation network. Analyzing the matching degree evaluation result: analyzing the read output result of the self-adaptive evaluation network to obtain the matching degree evaluation result of each operation activity to be distributed. And (5) storing a matching degree evaluation result: and storing the matching degree evaluation result obtained by analysis in a proper data structure or database for subsequent use or reporting. Through the steps, the output result of the self-adaptive evaluation network, namely the matching degree evaluation result, can be obtained and applied to task allocation or other related decisions so as to optimize the operation flow and improve the operation efficiency.
And completing the association binding with the operator through the matching degree evaluation result, distributing a work unit package, and performing operation digital management.
Specifically, a matching degree evaluation result is obtained: and obtaining a matching degree evaluation result of each job activity to be distributed, which is output by the self-adaptive evaluation network. Ordering and selection: and sorting the job activities to be allocated according to the matching degree evaluation result, and selecting the job activities with higher matching degree for preferential allocation. Association binding: and according to the operator files and the matching degree evaluation results, carrying out association binding on the to-be-allocated operation activities and operators with corresponding skill levels and trust degrees. This may be done by means of automatic dispensing or manual dispensing. Updating the activity state of the job: after the association binding is completed, updating the state of the job activity to be allocated, and recording the allocated worker information. Executing the job: and according to the result of the association binding, the operator starts to execute corresponding operation activities to complete the operation tasks. Monitoring and adjusting: in the process of executing the job, the progress condition of the task and the performance of the operator are continuously monitored, and the operation is adjusted according to the actual condition. This may include reassigning tasks, adjusting the job plan and task schedule of the job personnel, and the like. Through the steps, the association binding of the operation activities and the operators can be realized through the matching degree evaluation results, the digital management of the operation is completed, the accuracy and the efficiency of task allocation are improved, the project management cost is reduced, and the project is ensured to be completed on time and the quality reaches the standard.
Further, in the method of the present application, after the job personnel file and the job activity requirement to be allocated are input into the adaptive evaluation network, the method further includes:
performing collaborative record calling on the job activities to be allocated and the job personnel files to generate a calling result;
and generating matched additional factors through the calling result, and performing task matching degree evaluation compensation of the self-adaptive evaluation network by using the additional factors.
Specifically, the operation activities to be allocated and the operator files are subjected to collaborative record calling, a calling result is generated, matched additional factors are generated through the calling result, task matching degree evaluation compensation of the self-adaptive evaluation network is performed through the additional factors, and collaborative records are called: and extracting the cooperation records of the operators from the operator files, wherein the cooperation records comprise information such as performance, cooperation times, task completion quality and the like of the operators in past tasks. At the same time, relevant collaboration requirements and criteria, such as task content, required skills, etc., are extracted from the job activities to be distributed. Generating a calling result: and matching and calling the job activities to be allocated and the job personnel files according to the cooperation records and the cooperation demands, and generating a calling result. The call result may include an evaluation value of the degree of matching, the degree of suitability of the worker, and the like. Generating additional factors: and generating additional factors related to the matching degree and the suitability degree of the operator according to the calling result. The additional factor may be an adjustment value for compensating the task matching degree evaluation result of the adaptive evaluation network. Performing task matching degree evaluation compensation: and applying an additional factor to the output result of the adaptive evaluation network to compensate the task matching degree evaluation. This can be achieved by multiplying or adding additional factors to the evaluation result. Through the steps, the operation activities to be distributed and the worker files can be cooperatively recorded and called, a calling result is generated, and matched additional factors are generated to compensate the self-adaptive evaluation network, so that the matching degree of the tasks is more accurately evaluated, and the task distribution efficiency and accuracy are improved.
Further, as shown in fig. 2, the method of the present application further includes:
establishing a skill matching sub-network, a task main body adaptation degree sub-network, a time limit adaptation sub-network and a weight distribution network, and constructing a self-adaptive evaluation network by the skill matching sub-network, the task main body adaptation degree sub-network, the time limit adaptation sub-network and the weight distribution network;
after the worker files and the to-be-allocated operation activity demands are input into a self-adaptive evaluation network, carrying out data analysis and calling through the skill matching sub-network, the task main body adaptation degree sub-network and the time limit adaptation sub-network, and executing sub-network evaluation;
and carrying out adaptation integration of the sub-network evaluation results by the weight distribution network, and outputting matching degree evaluation results.
Specifically, a sub-network and a weight distribution network are defined: according to the requirements of task matching degree evaluation, a skill matching sub-network, a task main body matching degree sub-network, a time limit matching sub-network and a weight distribution network are defined. Each subnetwork may be a neural network or machine learning model for processing a particular assessment task. The weight distribution network may be a weight distribution model for assigning corresponding weights according to different evaluation factors. Constructing an adaptive evaluation network: and constructing a skill matching sub-network, a task body adaptation degree sub-network, a time limit adaptation sub-network and a weight distribution network into a self-adaptive evaluation network. The adaptive evaluation network may be a hierarchical structure, where each sub-network is responsible for handling a specific evaluation task and passing the result to the next sub-network or weight distribution network for processing. Inputting an operator file and job activity requirements to be distributed: and inputting the worker files and the to-be-allocated job activity demands into the adaptive evaluation network. Each sub-network performs a specific assessment task according to the input operator files and the job activity requirements to be allocated. Data parsing calls and performs sub-network evaluation: each sub-network analyzes and calls the input data and executes corresponding evaluation tasks. This may include the skill matching sub-network evaluating the degree of matching of worker skill and task requirements, the task body fitness sub-network evaluating the compatibility of workers and task bodies, the time limit fitness sub-network evaluating the degree of matching of task time limits and worker work plans, etc. Through the steps, a skill matching sub-network, a task main body adaptation degree sub-network, a time limit adaptation sub-network and a weight distribution network can be established, an adaptive evaluation network is established, and an operator file and the to-be-distributed operation activity requirement are input into the adaptive evaluation network for evaluation. And then, carrying out adaptation integration through the evaluation results of all the sub-networks, and outputting a final matching degree evaluation result so as to guide task allocation or other related decisions.
Further, as shown in fig. 3, the method of the present application further includes:
establishing a clustering feature set, wherein the clustering feature set comprises position features, skill features and task association features;
taking the clustering feature set as a matching feature, carrying out multi-level granularity clustering on the unit packets in the operation activity space, and generating a multi-level granularity clustering result;
performing task state evaluation of the operators by using the operator files, and generating a combined matching instruction if a preset threshold is met;
controlling the worker file and the multi-level granularity clustering result to carry out combined task matching through the combined matching instruction;
and carrying out job digital management according to the combined task matching result.
Specifically, a cluster feature set is established: and establishing a clustering feature set according to the requirements and the characteristics of task allocation. The feature set includes location features, skill features, and task association features. The location characteristics may include the geographic location of the operator, the workplace, etc.; skill characteristics may include skill level of the operator, field of expertise, etc.; the task association features may include information about tasks that the operator has previously performed, associations between tasks, and the like. Performing multistage granularity clustering: and taking the clustering feature set as a matching feature, and carrying out multi-level granularity clustering of the unit package in the operation activity space. This may include primary clustering of unit packages based on location features, skill features, and task association features, and advanced clustering based on task needs and worker features. Through multi-level granularity clustering, multi-level granularity clustering results can be generated. Task state evaluation of operators: and in the unit package multi-level granularity clustering process, the task state evaluation of the operators is carried out by using the operator files. And evaluating the current task state of the operator according to the skill level, the trust level, the task completion condition and the like of the operator. And if the preset threshold is met, generating a combined matching instruction. Combining task matching: and according to the combined matching instruction, matching the worker file with the multi-level granularity clustering result. This may include matching a worker of a particular skill with a corresponding task unit package, or matching a group of workers with a group of task unit packages, etc. By combining task matching, more accurate task allocation is realized. And (3) job digital management: and performing job digital management according to the combined task matching result. This may include updating worker profiles, adjusting mission plans, monitoring mission progress, collecting feedback and ratings, and the like. Through the digital management of the operation, the project management efficiency and quality can be improved. Through the steps, the clustering feature set can be established and multi-level granularity clustering can be carried out, so that more accurate task allocation is realized. Meanwhile, the worker files are combined to perform worker task state evaluation, and a combined matching instruction is generated to control the worker files to perform combined task matching with the multi-level granularity clustering result. And finally, performing job digital management according to the combined task matching result, and improving the efficiency and accuracy of project management.
Further, the method of the present application further comprises:
performing operation grid division on the task space, and generating real-time operation positions of operators through GIS equipment;
setting random verification constraint, calling GIS equipment of an operator through the random verification constraint, and executing task verification;
obtaining a task verification result, wherein the task verification result comprises a task execution progress verification result and a task state verification result;
performing execution risk prediction of sequential tasks according to the real-time operation position and the task verification result;
and performing task mobilization management according to the execution risk prediction result.
Specifically, job meshing: the task space is divided into a plurality of grid cells, and each grid cell represents a working area or working point. Thus, the task space can be conveniently and uniformly managed and scheduled. Generating a real-time operation position: and generating the real-time operation position of the operator through the GIS equipment. The GIS equipment can be positioning equipment, such as a GPS (global positioning system) positioner, a Beidou positioner and the like, and can acquire the position information of an operator in real time. And matching the position information of the operator with the operation grid, and determining the grid unit and the operation area where the operator is located. Random verification constraint setting: to ensure accuracy and fairness of task approval, random verification constraints may be set. The random verification constraint may be an algorithm or program for invoking the operator GIS device to perform task verification. In the task verification process, the randomness and the security of the verification process are ensured by using random numbers or encryption technology and other modes. Task verification: after setting random verification constraint, executing task verification by calling the GIS equipment of the operator. Task approval may include approval of execution progress of a task and task status. And according to the operation grid and the real-time operation position of the operator, and combining the task requirements and standards, evaluating and verifying the execution condition of the task. Task verification result acquisition: the task verification result can be obtained through the task verification process. The task verification results comprise a task execution progress verification result and a task state verification result. The results can be evaluation and feedback of the task completion condition of the operators, and provide basis for subsequent task scheduling and management. Performing risk prediction of sequential tasks: according to the real-time operation position and the task verification result, the execution risk of the sequential tasks can be further predicted. Sequential tasks refer to a series of tasks that need to be completed in a certain order. And predicting the execution risk of the sequential tasks by analyzing the information such as the task density, the workload, the historical task execution condition and the like of the operation grid where the operator is located. Task orchestration management: and performing task mobilization management according to the execution risk prediction result of the sequential tasks. This may include reassignment of tasks, adjusting job priorities, providing support and assistance, and the like. The task transferring management aims to ensure smooth completion of tasks, reduce execution risk and improve operation efficiency and quality. Through the steps, the task space can be subjected to operation grid division, the real-time operation position of an operator is generated, random verification constraint is set, and task verification is executed. The obtained task verification results comprise task execution progress verification results and task state verification results, and can be used for execution risk prediction of sequential tasks. And performing task scheduling management according to the execution risk prediction result so as to optimize the task completion mode and improve the project management effect.
Further, the method of the present application further comprises:
acquiring task priority of the job activity;
generating a risk prediction influence factor according to the task priority of the sequential tasks when performing execution risk prediction of the sequential tasks, and executing task execution steady state judgment;
if the judging result is lower than the dangerous threshold value, generating a mobilization instruction;
and controlling the sequential tasks to carry out reset matching according to the mobilizing instruction.
Specifically, the task priority of the job activity is acquired: in the task allocation process, corresponding task priorities are set for each job activity according to factors such as task requirements, emergency degree, difficulty and the like. The task priority may be a numerical value or a level indicating the urgency and importance of the task. Performing risk prediction of sequential tasks: in performing execution risk prediction of sequential tasks, the influence of task priority needs to be considered. And generating corresponding risk prediction influence factors according to the acquired task priorities of the sequential tasks. The risk prediction influencing factor may be a value associated with the task priority, for indicating the contribution of the task priority to performing the risk prediction. Task execution steady state determination: after the risk prediction influencing factors are generated, task execution steady state determination may be performed. The task execution steady state determination is to evaluate and determine the execution stability of the sequential tasks according to risk prediction influencing factors and other related factors (such as worker skills, historical performances and the like). Processing when the determination result is lower than the risk threshold: if the judging result is lower than the dangerous threshold, the task execution is indicated to have larger risk and instability, and corresponding processing is needed. At this time, an mobilization instruction may be generated to instruct operations such as matching of the reset of the task or adjusting the priority of the task. According to the mobilizing instruction, controlling the sequential tasks to carry out reset matching: according to the generated mobilization instruction, the sequential tasks can be controlled to carry out reset matching. Resetting the matches may include reassigning tasks, adjusting task priorities, providing additional support, etc. to ensure successful completion of tasks and successful performance of projects. Through the steps, the influence of the task priority can be considered when the execution risk prediction of the sequential tasks is carried out, and corresponding mobilization instructions are generated according to the judging result so as to control the sequential tasks to carry out reset matching. Therefore, the execution process of the task can be better managed, the execution risk is reduced, and the project management effect is improved.
Further, the method of the present application further comprises:
generating a visual instruction, and performing visual prompt through the visual instruction in the process of executing field operation by an operator;
and acquiring an image by an operator according to the visual prompt, uploading the image to a corresponding operation activity, and completing visual management.
Specifically, the visualization instruction generation: and generating corresponding visual instructions according to the task demands and the field operation characteristics. The visual instructions may include graphic, image, text, etc. forms for providing the operator with references and prompts for the field operation. Visual prompting: and in the process of executing field operation by an operator, carrying out visual prompt through the visual instruction. These hints may include boundaries of the job area, location and status of the job object, notes during the job, and so forth. Through visual prompt, can help the operating personnel to understand and accomplish on-the-spot operation better. Image acquisition and uploading: and acquiring an image acquired by an operator according to the visual prompt, and uploading the image to a corresponding operation activity. Image acquisition may include taking pictures or video of the process, results, etc., and then uploading these image data to the corresponding work activities of the system. Visual management: through visual management, real-time monitoring and feedback of field operation can be realized. The manager can know the field operation condition of the operator through the visual instruction and the image data, so that the problems can be found and solved in time, and the accuracy and quality of the operation are ensured. Through the steps, the visual instruction can be generated, the visual prompt is carried out in the process of the operator executing the field operation, the image data collected by the operator according to the visual prompt is obtained, and the image data is uploaded to the corresponding operation activity, so that the visual management is realized. Therefore, the efficiency and the accuracy of on-site operation can be improved, the risk is reduced, and the smooth proceeding of projects is ensured.
Example two
Based on the same inventive concept as that of the digital management method for field operation of the foregoing embodiment, as shown in fig. 4, the present application provides a digital management system for field operation, the system comprising:
an automation creation module 10, wherein the automation creation module 10 is used for creating a job activity, the job activity stores task content, task time limit and skill requirements, and the job activity is automatically created through the task requirements;
a job activity demand generation module 20, where the job activity demand generation module 20 is configured to synchronize the job activity to a job activity space, and generate a job activity demand to be allocated;
the worker file building module 30 is used for building a worker file, wherein the worker file is a file built by historical operation data of interactive workers, and comprises a worker skill level file, a worker trust file and a real-time task file;
a requirement input module 40, wherein the requirement input module 40 is configured to input the operator profile and the requirement of the job activity to be allocated into an adaptive evaluation network, and execute task matching degree evaluation with task time limit as a limit constraint;
The matching degree evaluation result acquisition module 50 is configured to obtain a matching degree evaluation result, where the matching degree evaluation result is an output result of the adaptive evaluation network;
the digital management module 60 is used for carrying out association binding between the operation activities and the operators according to the matching degree evaluation result, so as to complete digital management of the operations.
Further, the system further comprises:
the evaluation compensation module is used for carrying out collaborative record calling on the to-be-allocated operation activities and the operator files to generate a calling result; and generating matched additional factors through the calling result, and performing task matching degree evaluation compensation of the self-adaptive evaluation network by using the additional factors.
Further, the system further comprises:
the evaluation result output module is used for establishing a skill matching sub-network, a task main body adaptation degree sub-network, a time limit adaptation sub-network and a weight distribution network, and constructing a self-adaptive evaluation network by the skill matching sub-network, the task main body adaptation degree sub-network, the time limit adaptation sub-network and the weight distribution network; after the worker files and the to-be-allocated operation activity demands are input into a self-adaptive evaluation network, carrying out data analysis and calling through the skill matching sub-network, the task main body adaptation degree sub-network and the time limit adaptation sub-network, and executing sub-network evaluation; and carrying out adaptation integration of the sub-network evaluation results by the weight distribution network, and outputting matching degree evaluation results.
Further, the system further comprises:
the digital management module is used for establishing a clustering feature set according to the result, wherein the clustering feature set comprises position features, skill features and task association features; taking the clustering feature set as a matching feature, carrying out multi-level granularity clustering on the unit packets in the operation activity space, and generating a multi-level granularity clustering result; performing task state evaluation of the operators by using the operator files, and generating a combined matching instruction if a preset threshold is met; controlling the worker file and the multi-level granularity clustering result to carry out combined task matching through the combined matching instruction; and carrying out job digital management according to the combined task matching result.
Further, the system further comprises:
the task mobilizing management module is used for carrying out operation grid division on the task space and generating real-time operation positions of operators through GIS equipment; setting random verification constraint, calling GIS equipment of an operator through the random verification constraint, and executing task verification; obtaining a task verification result, wherein the task verification result comprises a task execution progress verification result and a task state verification result; performing execution risk prediction of sequential tasks according to the real-time operation position and the task verification result; and performing task mobilization management according to the execution risk prediction result.
Further, the system further comprises:
the reset matching module is used for obtaining the task priority of the job activity; generating a risk prediction influence factor according to the task priority of the sequential tasks when performing execution risk prediction of the sequential tasks, and executing task execution steady state judgment; if the judging result is lower than the dangerous threshold value, generating a mobilization instruction; and controlling the sequential tasks to carry out reset matching according to the mobilizing instruction.
Further, the system further comprises:
the visual management module is used for generating visual instructions, and performing visual prompts through the visual instructions in the process of executing field operation by operators; and acquiring an image by an operator according to the visual prompt, uploading the image to a corresponding operation activity, and completing visual management.
The foregoing detailed description of a digital management method for field operation will be clear to those skilled in the art, and for the system disclosed in this embodiment, the description is relatively simple, and the relevant places refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A method for digitally managing field operations, the method comprising:
acquiring a job activity, wherein the job activity stores a job type, job content, a job object, task time limit, post skills, a metering mode and frequency, and the job activity is automatically created through task requirements;
synchronizing the job activities to a job activity space to generate job requirements to be distributed;
establishing an operator file, wherein the operator file is a file constructed by historical operation data of interactive operators, and comprises an operator skill level file, an operator trust file and a real-time task file;
Inputting the worker files and the job requirements to be distributed into an adaptive evaluation network, and executing task matching degree evaluation taking task time limit as limit constraint;
obtaining a matching degree evaluation result, wherein the matching degree evaluation result is an output result of the self-adaptive evaluation network;
and completing the association binding with the operator through the matching degree evaluation result, distributing a work unit package, and performing operation digital management.
2. The method of claim 1, wherein said inputting the job personnel profile and the job requirements to be distributed into an adaptive evaluation network further comprises:
performing collaborative record calling on the job activities to be allocated and the job personnel files to generate a calling result;
and generating matched additional factors through the calling result, and performing task matching degree evaluation compensation of the self-adaptive evaluation network by using the additional factors.
3. The method of claim 2, wherein the method further comprises:
establishing a skill matching sub-network, a task main body adaptation degree sub-network, a time limit adaptation sub-network and a weight distribution network, and constructing a self-adaptive evaluation network by the skill matching sub-network, the task main body adaptation degree sub-network, the time limit adaptation sub-network and the weight distribution network;
After the worker files and the to-be-allocated operation activity demands are input into a self-adaptive evaluation network, carrying out data analysis and calling through the skill matching sub-network, the task main body adaptation degree sub-network and the time limit adaptation sub-network, and executing sub-network evaluation;
and carrying out adaptation integration of the sub-network evaluation results by the weight distribution network, and outputting matching degree evaluation results.
4. A method as claimed in claim 3, wherein the method further comprises:
establishing a clustering feature set, wherein the clustering feature set comprises position features, skill features and task association features;
taking the clustering feature set as a matching feature, carrying out multi-level granularity clustering on the unit packets in the operation activity space, and generating a multi-level granularity clustering result;
performing task state evaluation of the operators by using the operator files, and generating a combined matching instruction if a preset threshold is met;
controlling the worker file and the multi-level granularity clustering result to carry out combined task matching through the combined matching instruction;
and carrying out job digital management according to the combined task matching result.
5. The method of claim 4, wherein the method further comprises:
Performing operation grid division on the task space, and generating real-time operation positions of operators through GIS equipment;
setting random verification constraint, calling GIS equipment of an operator through the random verification constraint, and executing task verification;
obtaining a task verification result, wherein the task verification result comprises a task execution progress verification result and a task state verification result;
performing execution risk prediction of sequential tasks according to the real-time operation position and the task verification result;
and performing task mobilization management according to the execution risk prediction result.
6. The method of claim 5, wherein the method further comprises:
acquiring task priority of the job activity;
generating a risk prediction influence factor according to the task priority of the sequential tasks when performing execution risk prediction of the sequential tasks, and executing task execution steady state judgment;
if the judging result is lower than the dangerous threshold value, generating a mobilization instruction;
and controlling the sequential tasks to carry out reset matching according to the mobilizing instruction.
7. The method of claim 1, wherein the method further comprises:
generating a visual instruction, and performing visual prompt through the visual instruction in the process of executing field operation by an operator;
And acquiring an image by an operator according to the visual prompt, uploading the image to a corresponding operation activity, and completing visual management.
8. A digital management system for field operations, the system comprising:
the automatic creation module is used for acquiring a job activity, wherein the job activity stores a job type, job content, a job object, task time limit, post skills, a metering mode and frequency, and the job activity is automatically created through task requirements;
the job demand generation module is used for synchronizing the job activities to a job activity space and generating job demands to be distributed;
the system comprises an operator file establishing module, a real-time task file and a user file establishing module, wherein the operator file establishing module is used for establishing an operator file, the operator file is a file constructed by historical operation data of interactive operators, and the operator file comprises an operator skill level file, an operator trust file and a real-time task file;
the matching degree evaluation execution module is used for inputting the operator files and the job requirements to be distributed into the self-adaptive evaluation network and executing task matching degree evaluation taking task time limit as limit constraint;
The evaluation result acquisition module is used for acquiring a matching degree evaluation result, wherein the matching degree evaluation result is an output result of the self-adaptive evaluation network;
and the digital management module is used for completing the association binding with the operators through the matching degree evaluation result, distributing the working unit package and carrying out the digital management of the operations.
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